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Junping Zhang, Fei-Yue Wang, Kunfeng Wang, Wei-Hua Lin, Xin Xu, Cheng Chen (2011)
Data-Driven Intelligent Transportation Systems: A SurveyIEEE Transactions on Intelligent Transportation Systems, 12
D. Dan, Liangfu Ge, Xing-Fei Yan (2019)
Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine visionMeasurement
Tao Mei, Y. Rui, Shipeng Li, Q. Tian (2014)
Multimedia search reranking: A literature surveyACM Comput. Surv., 46
Yu Zheng, L. Capra, O. Wolfson, Hai Yang (2014)
Urban Computing: Concepts, Methodologies, and ApplicationsACM Trans. Intell. Syst. Technol., 5
Xinchen Liu, Wu Liu, Huadong Ma, Huiyuan Fu (2016)
Large-scale vehicle re-identification in urban surveillance videos2016 IEEE International Conference on Multimedia and Expo (ICME)
Dominik Zapletal, A. Herout (2016)
Vehicle Re-identification for Automatic Video Traffic Surveillance2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Sin-Yu Chen, J. Hsieh, Jui-Chen Wu, Yung-Sheng Chen (2009)
Vehicle Retrieval Using Eigen Color and Multiple Instance Learning2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
C. Lan, Hui Li, J. Ou (2011)
Traffic load modelling based on structural health monitoring dataStructure and Infrastructure Engineering, 7
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun (2015)
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 39
Yuqing Gao, K. Mosalam (2018)
Deep Transfer Learning for Image‐Based Structural Damage RecognitionComputer‐Aided Civil and Infrastructure Engineering, 33
Yi Tang, Di Wu, Zhi Jin, Wenbin Zou, Xia Li (2017)
Multi-modal metric learning for vehicle re-identification in traffic surveillance environment2017 IEEE International Conference on Image Processing (ICIP)
Zhicheng Chen, Y. Bao, Jiahui Chen, Hui Li (2019)
Modelling the spatial distribution of heavy vehicle loads on long-span bridges based on undirected graphical modelStructure and Infrastructure Engineering, 15
Karric Kwong, R. Kavaler, R. Rajagopal, P. Varaiya (2009)
Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensorsTransportation Research Part C-emerging Technologies, 17
D. Lowe (2004)
Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 60
Xinchen Liu, Wu Liu, Tao Mei, Huadong Ma (2016)
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
C. Dong, O. Celik, F. Catbas, E. O'brien, S. Taylor (2020)
Structural displacement monitoring using deep learning-based full field optical flow methodsStructure and Infrastructure Engineering, 16
Bernhard Wrobel (2001)
Multiple View Geometry in Computer VisionKünstliche Intell., 15
C. Dong, F. Catbas (2019)
A non-target structural displacement measurement method using advanced feature matching strategyAdvances in Structural Engineering, 22
Zhicheng Chen, Hui Li, Y. Bao, Nan Li, Yao Jin (2016)
Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technologyStructural Control and Health Monitoring, 23
Haiyun Guo, Chaoyang Zhao, Zhiwei Liu, Jinqiao Wang, Hanqing Lu (2018)
Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification
E. Rosten, T. Drummond (2006)
Machine Learning for High-Speed Corner Detection
Sultan Khan, H. Ullah (2019)
A survey of advances in vision-based vehicle re-identificationComput. Vis. Image Underst., 182
Lucia-Georgiana Coca, Ștefan Romanescu, S. Botez, Adrian Iftene (2020)
Crack detection system in AWS Cloud using Convolutional neural networks
Matthew Brown, D. Lowe (2007)
Automatic Panoramic Image Stitching using Invariant FeaturesInternational Journal of Computer Vision, 74
M. Fischler, R. Bolles (1981)
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartographyCommun. ACM, 24
L. Brown (2010)
Example-Based Color Vehicle Retrieval for Surveillance2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Hong-Xing Yu, Ancong Wu, Weishi Zheng (2017)
Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification2017 IEEE International Conference on Computer Vision (ICCV)
G. Morgenthal, N. Hallermann (2014)
Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of StructuresAdvances in Structural Engineering, 17
Hieu Nguyen, L. Bai (2010)
Cosine Similarity Metric Learning for Face Verification
(2014)
A (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556
Gang Yang, Jianchao Wu, Qing Hu (2018)
Rapid detection of building cracks based on image processing technology with double square artificial marksAdvances in Structural Engineering, 22
A. Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas (2017)
Working hard to know your neighbor's margins: Local descriptor learning loss
Bo Zhang, Liming Zhou, Jian Zhang (2019)
A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer visionComputer‐Aided Civil and Infrastructure Engineering, 34
Jianqing Zhu, H. Zeng, Jingchang Huang, Shengcai Liao, Zhen Lei, C. Cai, Lixin Zheng (2018)
Vehicle Re-Identification Using Quadruple Directional Deep Learning FeaturesIEEE Transactions on Intelligent Transportation Systems, 21
Yan Bai, Yihang Lou, Feng Gao, Shiqi Wang, Yuwei Wu, Ling-yu Duan (2018)
Group-Sensitive Triplet Embedding for Vehicle ReidentificationIEEE Transactions on Multimedia, 20
E. Xing, A. Ng, Michael Jordan, Stuart Russell (2002)
Distance Metric Learning with Application to Clustering with Side-Information
Yun Zhou, Yilin Pei, Li Ziwei, Liang Fang, Yu Zhao, W. Yi (2020)
Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithmsMeasurement, 159
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2015)
Deep Residual Learning for Image Recognition2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Stefan Leutenegger, M. Chli, R. Siegwart (2011)
BRISK: Binary Robust invariant scalable keypoints2011 International Conference on Computer Vision
Elmar Mair, Gregory Hager, Darius Burschka, M. Suppa, G. Hirzinger (2010)
Adaptive and Generic Corner Detection Based on the Accelerated Segment Test
Jung-Eun Lee, Rong Jin, Anil Jain (2008)
Rank-based distance metric learning: An application to image retrieval2008 IEEE Conference on Computer Vision and Pattern Recognition
A. Krizhevsky, Ilya Sutskever, Geoffrey Hinton (2012)
ImageNet classification with deep convolutional neural networksCommunications of the ACM, 60
Vassileios Balntas, Karel Lenc, A. Vedaldi, K. Mikolajczyk (2017)
HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.
Advances in Structural Engineering – SAGE
Published: Dec 1, 2021
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